GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning.

This book constitutes the refereed proceedings of the First ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016. The 11 full papers presented were carefully reviewed and selected from 22 submissions.

This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action ...

... approach. However, user preferences are undisclosed and different from user to user. The current developments in machine learning ... process can adapt to the user. Current hardware capabilities allow to process a large amount of data ...

This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years.

... Gaussian process to include the preferences imposed by the ML and CL constraints. Recently, Pei et al. [9] propose a discriminative clustering model that uses relative comparisons and, like our method, can also make use of unspecified ...

... Gaussian Process Latent Variable Model (GPLVM) [51– 54] was studied. It is a non-parametric technique that can ... metric can be computed by using either pairwise loss or triplet loss. For the pairwise loss, its related methods ...

Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

... Gaussian process approach for preference learning , by Chu and Ghahramani [ 8 ] . Support Vector Classifier ( SVC ) . To apply SVC to preference learning , we first ... Approach to Preference Learning with Interaction Terms 839.

... Gaussian process models provide a probabilistic non-para- metric modelling approach for black-box identification of nonlinear dy- namic systems. The Gaussian processes can highlight areas of the in- put space where prediction quality is ...